The Paradox of Self-Interest in Multi-Agent Systems
In the realm of artificial intelligence, reinforcement learning (RL) has emerged as a powerful tool for training agents to make decisions in complex environments. However, when we introduce multiple agents into the mix, a fascinating yet unsettling phenomenon arises: the potential for self-interest to undermine global objectives.
The Pursuit of Personal Gain
In a multi-agent scenario, each agent's primary goal is to maximize its own reward function. This leads to a situation where each agent independently optimizes its own interests, without considering the broader implications for the system as a whole. This is known as the "tragedy of the commons" effect.
Paradoxical Situations
Consider a scenario where two agents are tasked with managing a shared resource, such as a water supply. Each agent's goal is to maximize its own water usage, without regard for the other agent's needs. As both agents pursue their individu...
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